Pengelompokan Kemiskinan di Indonesia Menggunakan Time Series Based Clustering - Dedi Setiawan; Amalia Zahra (Department of Statistics ITS, 2023)
Indonesia has a strong commitment in achieving the 2030 SDGs, which one of the targets is reducing poverty. Poverty itself is defined as the inability of an individual or group to meet the basic needs of both food and non-food. During the pandemic, Indonesia experienced an increase in poverty percentage, which peaked in September 2020 with 10.19%. The latest data in March 2022, showed that the number has decreased by 0.75% or equivalent of 1.79 million people. However, the decrease does not encompass all provinces, there are several provinces that still suffer from increasing poverty. Therefore, it is necessary to group the provinces in Indonesia based on its percentage of poverty, in order to provide more appropriate treatment. The analysis method used in this research is the time series-based clustering with dtw distance. The clustering algorithm used is hierarchical cluster complete linkage. Based on the analysis result, the use of dtw distance can increase the silhouette coefficient value to 0.75 compared to using the Euclidean distance. The silhouette coefficient is one of the parameters used to determine the goodness of the clustering results, where the value of 0.75 can already be said to be very good clustering results. The optimum result of the clustering is a total of 3 groups with low, medium, and high poverty categories, where NTT, Papua, and West Papua provinces have the highest poverty rates but have significant progress in reducing poverty.